Current and future public health is characterized by the increase of chronic and degenerative diseases, corresponding to the worldwide ageing of the population. The increasing prevalence of these conditions together with the long incubation period of the chronic diseases and the continual technological innovations, offer new opportunities to develop strategies for early diagnosis.
Public Health has an important mandate to critically assess the promises and the pitfalls of disease screening strategies. This MOOC will help you understand important concepts for screening programs that will be explored through a series of examples that are the most relevant to public health today. We will conclude with expert interviews that explore future topics that will be important for screening.
By the end of this MOOC, students should have the competency needed to be involved in the scientific field of screening, and understand the public health perspective in screening programs.
This MOOC has been designed by the University of Geneva and the University of Lausanne.
This MOOC has been prepared under the auspices of the Ecole romande de santé publique (www.ersp.ch) by Prof. Fred Paccaud, MD, MSc, Head of the Institute of Social and Preventive Medicine in Lausanne (www.iumsp.ch), in collaboration with Professor Antoine Flahault, MD, PhD, head of the Institute of Global Health, Geneva (https://www.unige.ch/medecine/isg/en/) and Prof. Gillian Bartlett-Esquilant (McGill University, Quebec/ Institute of Social and Preventive Medicine, Lausanne).

Avaliações

JG

very interesting. I learned a lot about diseases. Very informative course.

NA

Apr 30, 2020

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I am very happy and proud of my achievements, I will continue to learn.

Na lição

Screening Metrics

The second module, provided by Dr. Idris Guessous, will address the metrics of screening with concepts related to robustness, validity and impact. A quiz on screening metrics will complete this module.

Gillian Bartlett-Esquilant

Transcrição

Okay, let's take a few more minutes talking about sensitivity, specificity, and predictive value. You have seen on the slide you can compute these performance markers using formula a, b, c, d, and you will remember using the two by two table. Usually, you would put the people with the disease, without the disease, in a two buy two tables like that and you'll see the positive test and the negative test, and use the a, b, c, d formula. I strongly recommend not to strictly rely on the formula using, for example, sensitivity as a over a produce c, because once we have another set-up, imagine instead of disease, no disease here and test, no test, I would put people with the disease, without the disease here. Not only I changed the way disease are from here to here, but I inversed the data. I still use people with positive test and negative test. If you strictly used your formula a, b, c, d, you can really be in trouble calculating sensitivity, specificity, and even predictive value because all the definitions will not apply. If you do remember, for example, that sensitivity has a probability of having a positive test, given that you have the disease, if you can phrase that in your mind, you will never be in trouble calculating, for example, sensitivity. What is given here is the disease. This group of people, they have the disease, 100 people here, 200 people here, for example, and basically it will be people who are testing positive, these people over the sum of these two cells. So, whatever the way you have illustrated your table, you will always be able to calculate sensitivity, specificity, even negative predictive value because what will be given in the predictive value will be the results of the test, and if you can remember that in the other formula in term of algebraic or probabilistic term, you will never be in trouble computing statistical tests of sensitivity, specificity, and predictive value.